通过聚类-预测框架，显著提升多端口散射体负载预测精度。
Adjustment of Cluster-Then-Predict Framework for Multiport Scatterer Load Prediction
- 先聚类后预测，捕捉S参数与阻抗间的内在关系。
- 相比基线模型，均方根误差降低最高达46%。
- 提出真实场景评估指标RUI，适配多目标优化分析。
多端口散射体中相互依赖的负载值预测因高维性和阻抗与散射能力间复杂依赖关系而极具挑战性，但对通信与测量系统设计至关重要。本文提出一种两阶段聚类-预测框架，用于多负载值预测任务。该方法有效捕捉了S参数与对应负载阻抗间的潜在函数关系，在梯度提升（GB）模型上相较基线实现最高46%的均方根误差（RMSE）下降，且在多种聚类与回归方法组合下均表现稳定。此外，本文提出真实世界统一指数（RUI），用于量化分析多个目标冲突、量纲不同的性能指标之间的权衡，适用于真实场景评估。基于RUI分析，确定K均值聚类与K近邻（KNN）结合为最优配置。
原文摘要 · Abstract (English)
Predicting interdependent load values in multiport scatterers is challenging due to high dimensionality and complex dependence between impedance and scattering ability, yet this prediction remains crucial for the design of communication and measurement systems. In this paper, we propose a two-stage cluster-then-predict framework for multiple load values prediction task in multiport scatterers. The proposed cluster-then-predict approach effectively captures the underlying functional relation between S-parameters and corresponding load impedances, achieving up to a 46% reduction in Root Mean Square Error (RMSE) compared to the baseline when applied to gradient boosting (GB). This improvement is consistent across various clustering and regression methods. Furthermore, we introduce the Real-world Unified Index (RUI), a metric for quantitative analysis of trade-offs among multiple metrics with conflicting objectives and different scales, suitable for performance assessment in realistic scenarios. Based on RUI, the combination of K-means clustering and k-nearest neighbors (KNN) is identified as the optimal setup for the analyzed multiport scatterer.
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